Analytics is often used to describe, predict, and/or improve businesses' performance. In this context, the term “analytics” generally refers to the discovery and/or communication of meaningful patterns of data (e.g., data related to businesses' transactions, sales, revenue, and/or relationships). In one example, a company may have a production server that stores a particular data set related to the company's structure and/or business dealings. In this example, the production server may facilitate copying the data set to an analytics engine (such as a HADOOP cluster) that performs analytics on the data set. This process of copying the data set to the analytics engine is traditionally referred to as Extract, Transform, and Load (ETL) for analytics.
Unfortunately, traditional ETL for analytics may have certain drawbacks and/or inefficiencies. For example, traditional ETL for analytics may consume a relatively high amount of network resources (such as processing power and/or bandwidth). Additionally or alternatively, traditional ETL for analytics may represent up to 80% of each analytics job. In other words, up to 80% of the time needed to perform each analytics job may be dedicated to traditional ETL. Accordingly, traditional ETL may lead to excessive resource consumption and/or prolonged processing times in connection with analytics jobs.
Traditional analytics engines may also have certain drawbacks and/or inefficiencies. For example, a traditional analytics engine may be unable to open and/or read certain monolithic files (such as EXCHANGE database files). As a result, the traditional analytics engine may be unable to perform analytics on data stored in such monolithic files.
The instant disclosure, therefore, identifies and addresses a need for systems and methods for facilitating analytics on data sets stored in remote monolithic files.